






'''simple docstring'''





from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Optional[int]			,       A					:			Any		)    ->   Dict:
			return [
			    int(1_0_0_0 * (box[0] / width)		),
			    int(1_0_0_0 * (box[1] / height)		),
			    int(1_0_0_0 * (box[2] / width)		),
			    int(1_0_0_0 * (box[3] / height)		),
			]
def __UpperCAmelCase   (					A					:			np.ndarray			,       A					:			Optional[str]			,       A					:			Optional[str] = None		)    ->   str:
			UpperCAmelCase_      :       str          =       tesseract_config if tesseract_config is not None else ''''''

			# apply OCR
			UpperCAmelCase_      :       Union[str, Any]          =       to_pil_image(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       pil_image.size
			UpperCAmelCase_      :       Optional[Any]          =       pytesseract.image_to_data(A			,       lang=A			,       output_type='''dict'''			,       config=A		)
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

			# filter empty words and corresponding coordinates
			UpperCAmelCase_      :       Any          =       [idx for idx, word in enumerate(A		) if not word.strip()]
			UpperCAmelCase_      :       int          =       [word for idx, word in enumerate(A		) if idx not in irrelevant_indices]
			UpperCAmelCase_      :       str          =       [coord for idx, coord in enumerate(A		) if idx not in irrelevant_indices]
			UpperCAmelCase_      :       List[Any]          =       [coord for idx, coord in enumerate(A		) if idx not in irrelevant_indices]
			UpperCAmelCase_      :       List[Any]          =       [coord for idx, coord in enumerate(A		) if idx not in irrelevant_indices]
			UpperCAmelCase_      :       Tuple          =       [coord for idx, coord in enumerate(A		) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			UpperCAmelCase_      :       Tuple          =       []
			for x, y, w, h in zip(A			,       A			,       A			,       A		):
						UpperCAmelCase_      :       Tuple          =       [x, y, x + w, y + h]
						actual_boxes.append(A		)

			# finally, normalize the bounding boxes
			UpperCAmelCase_      :       int          =       []
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(A			,       A			,       A		)		)

			assert len(A		) == len(A		), "Not as many words as there are bounding boxes"

			return words, normalized_boxes







class  snake_case__      (					UpperCamelCase):
					a_        =					["pixel_values"]




					def __init__(  self  :		List[Any]				, _A  :		bool = True				, _A  :		Dict[str, int] = None				, _A  :		PILImageResampling = PILImageResampling.BILINEAR				, _A  :		bool = True				, _A  :		Optional[str] = None				, _A  :		Optional[str] = ""				, **_A  :		Optional[int]				, )    ->							None:
								super().__init__(**_A      )
								UpperCAmelCase_      :       Optional[Any]          =       size if size is not None else {'''height''': 2_24, '''width''': 2_24}
								UpperCAmelCase_      :       str          =       get_size_dict(_A      )

								UpperCAmelCase_      :       List[str]          =       do_resize
								UpperCAmelCase_      :       int          =       size
								UpperCAmelCase_      :       Any          =       resample
								UpperCAmelCase_      :       Any          =       apply_ocr
								UpperCAmelCase_      :       Tuple          =       ocr_lang
								UpperCAmelCase_      :       Dict          =       tesseract_config




					def 		A      (  self  :		Any				, _A  :		np.ndarray				, _A  :		Dict[str, int]				, _A  :		PILImageResampling = PILImageResampling.BILINEAR				, _A  :		Optional[Union[str, ChannelDimension]] = None				, **_A  :		int				, )    ->							np.ndarray:
								UpperCAmelCase_      :       Union[str, Any]          =       get_size_dict(_A      )
								if "height" not in size or "width" not in size:
											raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"      )
								UpperCAmelCase_      :       str          =       (size['''height'''], size['''width'''])
								return resize(_A				, size=_A				, resample=_A				, data_format=_A				, **_A      )





					def 		A      (  self  :		Tuple				, _A  :		ImageInput				, _A  :		bool = None				, _A  :		Dict[str, int] = None				, _A  :		PILImageResampling = None				, _A  :		bool = None				, _A  :		Optional[str] = None				, _A  :		Optional[str] = None				, _A  :		Optional[Union[str, TensorType]] = None				, _A  :		ChannelDimension = ChannelDimension.FIRST				, **_A  :		List[str]				, )    ->							PIL.Image.Image:
								UpperCAmelCase_      :       Union[str, Any]          =       do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase_      :       Union[str, Any]          =       size if size is not None else self.size
								UpperCAmelCase_      :       List[Any]          =       get_size_dict(_A      )
								UpperCAmelCase_      :       Tuple          =       resample if resample is not None else self.resample
								UpperCAmelCase_      :       Dict          =       apply_ocr if apply_ocr is not None else self.apply_ocr
								UpperCAmelCase_      :       Dict          =       ocr_lang if ocr_lang is not None else self.ocr_lang
								UpperCAmelCase_      :       Any          =       tesseract_config if tesseract_config is not None else self.tesseract_config

								UpperCAmelCase_      :       Any          =       make_list_of_images(_A      )

								if not valid_images(_A      ):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

								if do_resize and size is None:
											raise ValueError('''Size must be specified if do_resize is True.'''      )

								# All transformations expect numpy arrays.
								UpperCAmelCase_      :       List[Any]          =       [to_numpy_array(_A      ) for image in images]

								if apply_ocr:
											requires_backends(self				, '''pytesseract'''      )
											UpperCAmelCase_      :       str          =       []
											UpperCAmelCase_      :       List[Any]          =       []
											for image in images:
														UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       apply_tesseract(_A				, _A				, _A      )
														words_batch.append(_A      )
														boxes_batch.append(_A      )

								if do_resize:
											UpperCAmelCase_      :       Optional[int]          =       [self.resize(image=_A				, size=_A				, resample=_A      ) for image in images]

								# flip color channels from RGB to BGR (as Detectron2 requires this)
								UpperCAmelCase_      :       Any          =       [flip_channel_order(_A      ) for image in images]
								UpperCAmelCase_      :       Optional[int]          =       [to_channel_dimension_format(_A				, _A      ) for image in images]

								UpperCAmelCase_      :       Any          =       BatchFeature(data={'''pixel_values''': images}				, tensor_type=_A      )

								if apply_ocr:
											UpperCAmelCase_      :       Any          =       words_batch
											UpperCAmelCase_      :       Optional[Any]          =       boxes_batch
								return data










'''simple docstring'''





_UpperCamelCase						:						List[str]  =					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def __UpperCAmelCase   (					A					:			int		)    ->   int:
			UpperCAmelCase_      :       str          =       0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
						number //= 1_0_0_0_0_0

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_UpperCamelCase						:						list[bool | None]  =					[None] * 10_000_000
_UpperCamelCase						:						List[str]  =					True
_UpperCamelCase						:						Tuple  =					False
def __UpperCAmelCase   (					A					:			int		)    ->   bool:

			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			UpperCAmelCase_      :       Dict          =       chain(next_number(A		)		)
			UpperCAmelCase_      :       int          =       number_chain

			while number < 1_0_0_0_0_0_0_0:
						UpperCAmelCase_      :       Optional[Any]          =       number_chain
						number *= 1_0

			return number_chain
def __UpperCAmelCase   (					A					:			int = 1_0_0_0_0_0_0_0		)    ->   int:
			for i in range(1			,       A		):
						if CHAINS[i] is None:
									chain(i + 1		)

			return CHAINS[:number].count(A		)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{solution() = }''')


